SoroushVahidi/scidocs huggingface dataset:
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
(2015)

Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features Remote Sens. 2015, 7 14681 from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the lowand mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.
Memory Leak Analysis by Contradiction
(2006)

We present a novel leak detection algorithm. To prove the absence of a memory leak, the algorithm assumes its presence and runs a backward heap analysis to disprove this assumption. We have implemented this approach in a memory leak analysis tool and used it to analyze several routines that manipulate linked lists and trees. Because of the reverse nature of the algorithm, the analysis can locally reason about the absence of memory leaks. We have also used the tool as a scalable, but unsound leak detector for C programs. The tool has found several bugs in larger programs from the SPEC2000 suite.
Sign language recognition using Microsoft Kinect
(2013)

In last decade lot of efforts had been made by research community to create sign language recognition system which provide a medium of communication for differently-abled people and their machine translations help others having trouble in understanding such sign languages. Computer vision and machine learning can be collectively applied to create such systems. In this paper, we present a sign language recognition system which makes use of depth images that were captured using a Microsoft Kinect® camera. Using computer vision algorithms, we develop a characteristic depth and motion profile for each sign language gesture. The feature matrix thus generated was trained using a multi-class SVM classifier and the final results were compared with existing techniques. The dataset used is of sign language gestures for the digits 0-9.
Injury Risk Quantification for Industrial Robots in Collaborative Operation with Humans
(2010)

While the topic of human-safe robots has an appreciable academic history, only today are various solutions to this challenge becoming economically relevant. We are witnessing a maturing of technology and market expectations, paired with initiatives in the area of standardization. One of the remaining hurdles to routine deployment of robots for collaborative operation with humans is obtaining a well-founded understanding of low-level injuries of the type that such robots can potentially inflict. For the assessment of collaborative robot designs and systems, the low-level injury potential must be unambiguously quantified. A classification scheme for holding such information is presented and an outlook on its use is given.
Acceptance Model in the Domains of LIS and Education : A Review of Selected Literature
(2017)

The Technology Acceptance Model (TAM) is a theoretical framework that is most extensively utilized in explaining an individual’s acceptance of an information technology or an information system. This study reviews numerous literature related to the TAM focusing on TAM applications in the domains of Library and Information Science (LIS) and Education. The different studies in these areas were evaluated to understand the modifications incorporated into this model. The study attempts provide insight on future trends in the technology acceptance model as well to help identify gaps in literature where future research could be conducted.
Risk Aversion and Expected-Utility Theory : A Calibration Theorem
(1998)

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
A review of empirical research on manufacturing flexibility
(2000)

Manufacturing flexibility is widely recognized as a critical component to achieving a competitive advantage in the marketplace. A comprehensive look at the empirical research pertaining to manufacturing flexibility highlights the very fragmented nature of this body of work. We present a comprehensive contingency-based framework for examining the content related issues involving the relationships and variables included in past studies. We also examine several important Ž . research designrmethodology issues e.g., sampling, data collection and measurement and propose solutions to some identified problems. q 2000 Elsevier Science B.V. All rights reserved.
Risk Management : Coordinating Corporate Investment and Financing Policies
(2007)

This paper develops a general framework for analyzing corporate risk management policies. We begin by observing that if external sources of finance are more costly to corporations than internally generated funds, there will typically be a benefit to hedging: hedging adds value to the extent that it helps ensure that a corporation has sufficient internal funds available to take advantage of attractive investment opportunities. We then argue that this simple observation has wide ranging implications for the design of risk management strategies. We delineate how these strategies should depend on such factors as shocks to investment and financing opportunities. We also discuss exchange rate hedging strategies for multinationals, as well as strategies involving "nonlinear" instruments like options. CORPORATIONS TAKE RISK MANAGEMENT very seriously—recent surveys find that risk management is ranked by financial executives as one of their most important objectives.^ Given its real-world prominence, one might guess that the topic of risk management would command a great deal of attention from researchers in finance, and that practitioners would therefore have a welldeveloped body of wisdom from which to draw in formulating hedging strategies. Such a guess would, however, be at best only partially correct. Finance theory does do a good job of instructing firms on the implementation of hedges. For example, if a refining company decides that it wants to use options to reduce its exposure to oil prices by a certain amount, a BlackScholes type model can help the company calculate the number of contracts needed. Indeed, there is an extensive literature that covers numerous practical aspects of what might be termed "hedging mechanics," from the computation of hedge ratios to the institutional peculiarities of individual contracts. Unfortunately, finance theory has had much less clear cut guidance to offer on the logically prior questions of hedging strategy: What sorts of risks * Froot is from Harvard and NBER, Scharfstein is from MIT and NBER, and Stein is from MIT and NBER. We thank Don Lessard, Tim Luehrman, Andre Perold, Raghuram Rajan, Julio Rotemberg, and Stew Myers for helpful discussions. We are also grateful to the IFSRC and the Center for Energy Policy Research at MIT, the Department of Research at Harvard Business School, the National Science Foundation, and Batterymarch Financial Management for generous financial support. ' See Rawls and Smithson (1990).
Financing Constraints and Corporate Investment
(2007)

EMPIRICAL models of business investment rely generally on the assumption of a "representative firm" that responds to prices set in centralized securities markets. Indeed, if all firms have equal access to capital markets, firms' responses to changes in the cost of capital or tax-based investment incentives differ only because of differences in investment demand. A firm's financial structure is irrelevant to investment because external funds provide a perfect substitute for internal capital. In general, with perfect capital markets, a firm's investment decisions are independent of its financial condition. An alternative research agenda, however, has been based on the view that internal and external capital are not perfect substitutes. According to this view, investment may depend on financial factors, such as the availability of internal finance, access to new debt or equity finance, or the functioning of particular credit markets. For example, a firm's internal cash flow may affect investment spending because of a "financ-
Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex
(2016)

Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.
Synergistic Analysis of Evolving Graphs
(2016)

Evolving graph processing involves repeating analyses, which are often iterative, over multiple snapshots of the graph corresponding to different points in time. Since the snapshots of an evolving graph share a great number of vertices and edges, traditional approaches that process these snapshots one at a time without exploiting this overlap contain much wasted effort on both data loading and computation, making them extremely inefficient. In this article, we identify major sources of inefficiencies and present two optimization techniques to address them. First, we propose a technique for amortizing the fetch cost by merging fetching of values for different snapshots of the same vertex. Second, we propose a technique for amortizing the processing cost by feeding values computed by earlier snapshots into later snapshots. We have implemented these optimizations in two distributed graph processing systems, namely, GraphLab and ASPIRE. Our experiments with multiple real evolving graphs and algorithms show that, on average fetch amortization speeds up execution of GraphLab and ASPIRE by 5.2× and 4.1× , respectively. Amortizing the processing cost yields additional average speedups of 2× and 7.9×, respectively.
Assessment of methodological quality of primary studies by systematic reviews: results of the metaquality cross sectional study.
(2005)

OBJECTIVES
To describe how the methodological quality of primary studies is assessed in systematic reviews and whether the quality assessment is taken into account in the interpretation of results.


DATA SOURCES
Cochrane systematic reviews and systematic reviews in paper based journals.


STUDY SELECTION
965 systematic reviews (809 Cochrane reviews and 156 paper based reviews) published between 1995 and 2002.


DATA SYNTHESIS
The methodological quality of primary studies was assessed in 854 of the 965 systematic reviews (88.5%). This occurred more often in Cochrane reviews than in paper based reviews (93.9% v 60.3%, P < 0.0001). Overall, only 496 (51.4%) used the quality assessment in the analysis and interpretation of the results or in their discussion, with no significant differences between Cochrane reviews and paper based reviews (52% v 49%, P = 0.58). The tools and methods used for quality assessment varied widely.


CONCLUSIONS
Cochrane reviews fared better than systematic reviews published in paper based journals in terms of assessment of methodological quality of primary studies, although they both largely failed to take it into account in the interpretation of results. Methods for assessment of methodological quality by systematic reviews are still in their infancy and there is substantial room for improvement.
An Ontology-Based Approach to Model-Driven Software Product Lines
(2009)

Software development in highly variable domains constrained by tight regulations and with many business concepts involved results in hard to deliver and maintain applications, due to the complexity of dealing with the large number of concepts provided by the different parties and system involved in the process. One way to tackle these problems is thru combining software product lines and model-driven software development supported by ontologies. Software product lines and model-driven approaches would promote reuse on the software artifacts and, if supported by an ontological layer, those artifacts would be domain-validated. We intend to create a new conceptual framework for software development with domain validated models in highly variable domains. To define such a framework we will propose a model that relates several dimensions and areas of software development thru time and abstraction levels. This model would guarantee to the software house traceability of components, domain validated artifacts, easy to maintain and reusable components, due to the relations and mappings we propose to establish in the conceptual framework, between the software artifacts and the ontology.
Beyond Recommender Systems: Helping People Help Each Other
(2001)

The Internet and World Wide Web have brought us into a world of endless possibiliti es: interactive Web sites to experience, music to li sten to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations.
Context-aware application scheduling in mobile systems: what will users do and not do next?
(2016)

Usage patterns of mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness can be the key to make mobile systems to become personalized and situation dependent in managing their resources. We first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find context-dependency in application usage patterns, for which we classify contexts in a personalized manner with unsupervised learning methods. Using the knowledge acquired, we develop a novel context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications in a timely manner. Our trace-driven simulations with 96 user traces demonstrate the benefits of CAS over existing algorithms. We also verify the practicality of CAS by implementing it on the Android platform.
Multimodal feature extraction and fusion for semantic mining of soccer video: a survey
(2012)

This paper presents a classified review of soccer video analysis works. The existing approaches in the aspects of highlight event detection, video summarization and retrieval based on video stream, ball and player tracking for provision of match statistics, technical and tactical analysis and application of different sources in soccer video analysis have been surveyed. In addition, some major existing commercial softwares developed for video analysis are introduced and compared. With regard to the existing challenge for automatic and realtime provision of video analysis, different computer vision approaches are discussed and compared. Audio, video and text feature extraction methods have been investigated and the future trends for improvement of the reviewed systems have been introduced in terms of response time optimization, increase of precision and eliminating the need of human intervention for video analysis.
Confusion Matrix
(2010)

A confusion matrix (Kohavi and Provost, 1998) contains information about actual and predicted classifications done by a classification system. Performance of such systems is commonly evaluated using the data in the matrix. The following table shows the confusion matrix for a two class classifier. The entries in the confusion matrix have the following meaning in the context of our study: ● a is the number of correct predictions that an instance is negative, ● b is the number of incorrect predictions that an instance is positive, ● c is the number of incorrect of predictions that an instance negative, and ● d is the number of correct predictions that an instance is positive. Predicted Negative Positive Actual Negative a b Positive c d
Continuous Electrowetting of Non-toxic Liquid Metal for RF Applications
(2014)

Continuous electrowetting (CEW) is demonstrated to be an effective actuation mechanism for reconfigurable radio frequency (RF) devices that use non-toxic liquid-metal tuning elements. Previous research has shown CEW is an efficient means of electrically inducing motion in a liquid-metal slug, but precise control of the slug's position within fluidic channels has not been demonstrated. Here, the precise positioning of liquid-metal slugs is achieved using CEW actuation in conjunction with channels designed to minimize the liquid-metal surface energy at discrete locations. This approach leverages the high surface tension of liquid metal to control its resting position with submillimeter accuracy. The CEW actuation and fluidic channel design were optimized to create reconfigurable RF devices. In addition, solutions for the reliable actuation of a gallium-based, non-toxic liquid-metal alloy (Galinstan) are presented that mitigate the tendency of the alloy to form a surface oxide layer capable of wetting to the channel walls, inhibiting motion. A reconfigurable slot antenna utilizing these techniques to achieve a 15.2% tunable frequency bandwidth is demonstrated.
The Impact of E-Commerce Announcements on the Market Value of Firms
(2001)

Firms are undertaking growing numbers of e-commerce initiatives and increasinglymaking significant investments required to participate in the growing online market. However, empirical support for the benefits to firms from e-commerce is weaker than glowing accounts in the popular press, based on anecdotal evidence, would lead us to believe. In this paper, we explore the following questions: What are the returns to shareholders in firms engaging in e-commerce? How do the returns to conventional, brick and mortar firms from e-commerce initiatives compare with returns to the new breed of net firms? How do returns from businessto-business e-commerce compare with returns from business-to-consumer e-commerce? How do the returns to e-commerce initiatives involving digital goods compare to initiatives involving tangible goods? We examine these issues using event study methodology and assess the cumulative abnormal returns to shareholders (CARs) for 251 e-commerce initiatives announced by firms between October and December 1998. The results suggest that e-commerce initiatives do indeed lead to significant positive CARs for firms’ shareholders. While the CARs for conventional firms are not significantly different from those for net firms, the CARs for businessto-consumer (B2C) announcements are higher than those for business-to-business (B2B) announcements. Also, the CARs with respect to e-commerce initiatives involving tangible goods are higher than for those involving digital goods. Our data were collected in the last quarter of 1998 during a unique bull market period and the magnitudes of CARs (between 4.9 and 23.4% for different subsamples) in response to e-commerce announcements are larger than those reported for a variety of other firm actions in prior event studies. This paper presents the first empirical test of the dot com effect, validating popular anticipations of significant future benefits to firms entering into e-commerce arrangements. (Event Study; Electronic Commerce;Market Value; Resource-Based View; Business-to-Business;Business-to-Consumer; Digital Goods; Tangible Goods)
Facebook C2C social commerce: A study of online impulse buying
(2016)

Facebook users are increasingly using the site to conduct commercial activities, by posting advertisements in groups and then buying or selling items from each other. This type of group is called as a C2C Facebook “buy and sell” group in the current work. Drawing from Latent State Trait theory, Heuristic Information Processing, and Observational Learning, we conducted an online field experiment to empirically investigate the effect of the information quality of the advertisement, the trait of the impulsiveness, and the number of “Likes” it receives on consumers’ urge to buy impulsively. The findings and implications of our study are discussed in the
Biconvex Relaxation for Semidefinite Programming in Computer Vision
(2016)

References [1] Wang, P., Shen, C., van den Hengel, A., A fast semidefinite approach to solving binary quadratic problems. CVPR 2013. [2] Candes, E.J., Li, X., Soltanolkotabi, M., Phase retrieval via wirtinger flow: Theory and algorithms. IEEE Transaction on Information Theory, 2015. [3] Zheng, Q., Lafferty, J., A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements. NIPS 2015. [4] Netrapalli, P., Jain, P., Sanghavi, S., Phase retrieval using alternating minimization. NIPS 2013. Many important problems requires solving for low rank SDPs with PSD constraint matrices.